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02/13/2023
Revet: A Language and Compiler for Dataflow Threads
Spatial dataflow architectures such as reconfigurable dataflow accelerat...
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12/01/2022
BaCO: A Fast and Portable Bayesian Compiler Optimization Framework
We introduce the Bayesian Compiler Optimization framework (BaCO), a gene...
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11/07/2022
Stardust: Compiling Sparse Tensor Algebra to a Reconfigurable Dataflow Architecture
We introduce Stardust, a compiler that compiles sparse tensor algebra to...
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08/31/2022
The Sparse Abstract Machine
We propose the Sparse Abstract Machine (SAM), an intermediate representa...
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06/11/2022
Homunculus: Auto-Generating Efficient Data-Plane ML Pipelines for Datacenter Networks
Support for Machine Learning (ML) applications in networks has significa...
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02/02/2022
Efficient Memory Partitioning in Software Defined Hardware
As programmers turn to software-defined hardware (SDH) to maintain a hig...
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04/26/2021
Capstan: A Vector RDA for Sparsity
This paper proposes Capstan: a scalable, parallel-patterns-based, reconf...
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06/25/2020
Prior-guided Bayesian Optimization
While Bayesian Optimization (BO) is a very popular method for optimizing...
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02/12/2020
Taurus: An Intelligent Data Plane
Emerging applications – cloud computing, the internet of things, and aug...
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09/26/2019
Serving Recurrent Neural Networks Efficiently with a Spatial Accelerator
Recurrent Neural Network (RNN) applications form a major class of AI-pow...
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05/31/2019
Efficient Multiway Hash Join on Reconfigurable Hardware
We propose the algorithms for performing multiway joins using a new type...
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05/24/2019
Polystore++: Accelerated Polystore System for Heterogeneous Workloads
Modern real-time business analytic consist of heterogeneous workloads (e...
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04/26/2019
DeepFreak: Learning Crystallography Diffraction Patterns with Automated Machine Learning
Serial crystallography is the field of science that studies the structur...
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10/11/2018
Practical Design Space Exploration
Multi-objective optimization is a crucial matter in computer systems des...
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06/04/2018
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark
The deep learning community has proposed optimizations spanning hardware...
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03/09/2018
High-Accuracy Low-Precision Training
Low-precision computation is often used to lower the time and energy cos...
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08/25/2017
LevelHeaded: Making Worst-Case Optimal Joins Work in the Common Case
Pipelines combining SQL-style business intelligence (BI) queries and lin...
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05/22/2017
Infrastructure for Usable Machine Learning: The Stanford DAWN Project
Despite incredible recent advances in machine learning, building machine...
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03/23/2017
Flare: Native Compilation for Heterogeneous Workloads in Apache Spark
The need for modern data analytics to combine relational, procedural, an...
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06/22/2015
Taming the Wild: A Unified Analysis of Hogwild!-Style Algorithms
Stochastic gradient descent (SGD) is a ubiquitous algorithm for a variet...
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11/05/2014
Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems
Stochastic gradient descent (SGD) on a low-rank factorization is commonl...
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06/27/2012